def createmodel(self, quantize=True):
"""Creates the model and attaches with the dataloader.
By default it sets up the model for quantization aware training.
Parameters
----------
quantize : bool, optional
To quantize or not, by default True
"""
print("Creating model..")
vision.learner.create_body = self.create_custom_body
self.learn = cnn_learner(
self.data,
models.mobilenet_v2,
pretrained=True,
metrics=[error_rate, FBeta(beta=1), Precision(), Recall(), AUROC()],
split_on=custom_split,
model_dir=self.model_dir,
)
if quantize:
self.learn.model[0].qconfig = torch.quantization.default_qat_qconfig
self.learn.model = torch.quantization.prepare_qat(
self.learn.model, inplace=True
)
def __get_objective_func_name(self):
from fastai.metrics import root_mean_squared_error, mean_squared_error, mean_absolute_error, accuracy, FBeta, AUROC, Precision, Recall, r2_score
metrics_map = {
# Regression
'root_mean_squared_error': root_mean_squared_error,
'mean_squared_error': mean_squared_error,
'mean_absolute_error': mean_absolute_error,
'r2': r2_score,
# Not supported: median_absolute_error
# Classification
'accuracy': accuracy,
'f1': FBeta(beta=1),
'f1_macro': FBeta(beta=1, average='macro'),
'f1_micro': FBeta(beta=1, average='micro'),
'f1_weighted': FBeta(beta=1, average='weighted'), # this one has some issues
'roc_auc': AUROC(),
'precision': Precision(),
'precision_macro': Precision(average='macro'),
'precision_micro': Precision(average='micro'),
'precision_weighted': Precision(average='weighted'),
'recall': Recall(),
'recall_macro': Recall(average='macro'),
'recall_micro': Recall(average='micro'),
'recall_weighted': Recall(average='weighted'),
'log_loss': None,
# Not supported: pac_score
}
# Unsupported metrics will be replaced by defaults for a given problem type
objective_func_name = self.stopping_metric.name
if objective_func_name not in metrics_map.keys():
if self.problem_type == REGRESSION:
objective_func_name = 'mean_squared_error'
else:
objective_func_name = 'log_loss'
logger.warning(f'Metric {self.stopping_metric.name} is not supported by this model - using {objective_func_name} instead')
if objective_func_name in metrics_map.keys():
nn_metric = metrics_map[objective_func_name]
else:
nn_metric = None
return nn_metric, objective_func_name